Development and Applications of Coarse-Grained Models for RNA

Iterative annealing mechanism explains the functions of the GroEL and RNA chaperones

Molecular chaperones are ATP-consuming machines, which facilitate the folding of proteins and RNA molecules that are kinetically trapped in misfolded states. Unassisted folding occurs by the kinetic partitioning mechanism according to which folding to the native state, with low probability as well as misfolding to one of the many metastable states, with high probability, occur rapidly. GroEL is an all-purpose stochastic machine that assists misfolded substrate proteins to fold. The RNA chaperones such as CYT-19, which are ATP-consuming enzymes, help the folding of ribozymes that get trapped in metastable states for long times. GroEL does not interact with the folded proteins but CYT-19 disrupts both the folded and misfolded ribozymes. The structures of GroEL and RNA chaperones are strikingly different. Despite these differences, the iterative annealing mechanism (IAM) quantitatively explains all the available experimental data for assisted folding of proteins and ribozymes. Driven by ATP binding and hydrolysis and GroES binding, GroEL undergoes a catalytic cycle during which it samples three allosteric states, T (apo), R (ATP bound), and R^″ (ADP bound). Analyses of the experimental data show that the efficiency of the GroEL-GroES machinery and mutants is determined by the resetting rate k _{R ″  → T} , which is largest for the wild-type (WT) GroEL. Generalized IAM accurately predicts the folding kinetics of Tetrahymena ribozyme and its variants. Chaperones maximize the product of the folding rate and the steady-state native state fold by driving the substrates out of equilibrium. Neither the absolute yield nor the folding rate is optimized.

Prediction of solution properties and dynamics of RNAs by means of Brownian dynamics simulation of coarse-grained models: Ribosomal 5S RNA and phenylalanine transfer RNA

Background

The possibility of validating biological macromolecules with locally disordered domains like RNA against solution properties is helpful to understand their function. In this work, we present a computational scheme for predicting global properties and mimicking the internal dynamics of RNA molecules in solution. A simple coarse-grained model with one bead per nucleotide and two types of intra-molecular interactions (elastic interactions and excluded volume interactions) is used to represent the RNA chain. The elastic interactions are modeled by a set of Hooke springs that form a minimalist elastic network. The Brownian dynamics technique is employed to simulate the time evolution of the RNA conformations.

Results

That scheme is applied to the 5S ribosomal RNA of E. Coli and the yeast phenylalanine transfer RNA. From the Brownian trajectory, several solution properties (radius of gyration, translational diffusion coefficient, and a rotational relaxation time) are calculated. For the case of yeast phenylalanine transfer RNA, the time evolution and the probability distribution of the inter-arm angle is also computed.

Conclusions

The general good agreement between our results and some experimental data indicates that the model is able to capture the tertiary structure of RNA in solution. Our simulation results also compare quite well with other numerical data. An advantage of the scheme described here is the possibility of visualizing the real time macromolecular dynamics.

Electronic supplementary material

The online version of this article (doi:10.1186/s13628-015-0025-7) contains supplementary material, which is available to authorized users.

Generalized Manning Condensation Model Captures the RNA Ion Atmosphere

RNA is highly sensitive to the ionic environment and typically requires Mg(2+) to form compact structures. There is a need for models capable of describing the ion atmosphere surrounding RNA with quantitative accuracy. We present a model of RNA electrostatics and apply it within coarse-grained molecular dynamics simulation. The model treats Mg(2+) ions explicitly to account for ion-ion correlations neglected by mean-field theories. Since mean-field theories capture KCl well, it is treated implicitly by a generalized Manning counterion condensation model. The model extends Manning condensation to deal with arbitrary RNA conformations, nonlimiting KCl concentrations, and the ion inaccessible volume of RNA. The model is tested against experimental measurements of the excess Mg(2+) associated with the RNA, Γ(2+), because Γ(2+) is directly related to the Mg(2+)-RNA interaction free energy. The excellent agreement with experiment demonstrates that the model captures the ionic dependence of the RNA free energy landscape.